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Arrhythmia Detection in ECG Signal Using Fractional Wavelet Transform with Principal Component Analysis
Any significant alteration in the Electro-Cardio-Gram (ECG) signal wave components (P-QRS-T) for a time duration is detected as arrhythmia. In this paper, a novel fractional wavelet transform (FrWT) is used as a preprocessing tool. FrWT describes the given signal in time–frequency fractional domain using fractional Fourier transform and its denoising using wavelet transform. Because of this novel and intriguing property, it is broadly utilized as a noise removal tool in the fractional domain along with multiresolution analysis. Next, features are extracted using Yule–Walker autoregressive modeling. Dimensionality of the extracted features is to be reduced for proper detection of different types of arrhythmias. Principal component analysis has been applied for arrhythmia detection using variance estimation. The proposed method is evaluated on the basis of various performance parameters such as output SNR, mean squared error (MSE) and detection accuracy (DEAcc). An output SNR of 33.41 dB, MSE of 0.1689% and Acc of 99.94% for real-time ECG database and output SNR of 25.25 dB, MSE of 0.1656%, DEAcc of 99.89% for MIT-BIH Arrhythmia database are obtained.
Arrhythmia Detection in ECG Signal Using Fractional Wavelet Transform with Principal Component Analysis
Any significant alteration in the Electro-Cardio-Gram (ECG) signal wave components (P-QRS-T) for a time duration is detected as arrhythmia. In this paper, a novel fractional wavelet transform (FrWT) is used as a preprocessing tool. FrWT describes the given signal in time–frequency fractional domain using fractional Fourier transform and its denoising using wavelet transform. Because of this novel and intriguing property, it is broadly utilized as a noise removal tool in the fractional domain along with multiresolution analysis. Next, features are extracted using Yule–Walker autoregressive modeling. Dimensionality of the extracted features is to be reduced for proper detection of different types of arrhythmias. Principal component analysis has been applied for arrhythmia detection using variance estimation. The proposed method is evaluated on the basis of various performance parameters such as output SNR, mean squared error (MSE) and detection accuracy (DEAcc). An output SNR of 33.41 dB, MSE of 0.1689% and Acc of 99.94% for real-time ECG database and output SNR of 25.25 dB, MSE of 0.1656%, DEAcc of 99.89% for MIT-BIH Arrhythmia database are obtained.
Arrhythmia Detection in ECG Signal Using Fractional Wavelet Transform with Principal Component Analysis
J. Inst. Eng. India Ser. B
Gupta, Varun (author) / Mittal, Monika (author)
Journal of The Institution of Engineers (India): Series B ; 101 ; 451-461
2020-10-01
11 pages
Article (Journal)
Electronic Resource
English
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